Convolutional Sequence to Sequence Model for Human Dynamics
Open accessAbstract
Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated dynamics.We present a novel approach to human motion modeling based on convolutional neural networks (CNN). The hierarchical structure of CNN makes it capable of capturing both spatial and temporal correlations effectively. In our proposed approach, a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable, which is used with a decoder to predict the remainder of the sequence. The decoder itself also has an encoder-decoder structure, in which the short-term encoder encodes a shorter sequence to a short-term hidden variable, and the spatial decoder maps the long and short-term hidden variable to motion predictions. By using such a model, we are able to capture both invariant and dynamic information of human motion, which results in more accurate predictions. Experiments show that our algorithm outperforms the state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our code is available at the project website.
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Deep learning technologies including CNN, RNN, and LSTM have been widely applied to motion intent prediction.
“Deep learning, technologies including Convolutional Neural Network (CNN) 15,16 , Recurrent Neural Network (RNN) [17][18][19] , and Long Short-Term Memory network (LSTM) [20][21][22] have been widely applied to motion intent prediction.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [16]
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- Authors
- Li, C., Zhang, Z., Lee, W. S., & Lee, G. H.
- Affiliations
- National University of Singapore (SG)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5226-5234
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963548793
- Cited by
- 364
- References
- 21
- Citations by year
- 2026: 16 · 2025: 41 · 2024: 56 · 2023: 75 · 2022: 51 · 2021: 57 · 2020: 41 · 2019: 24 · 2018: 3
- FWCI
- 46.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National University of Singapore (MOE Tier 1, Tier 1)
- Ministry of Education, India
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- human motion modeling, human dynamics, sequence-to-sequence prediction, convolutional neural networks, encoder-decoder architecture, spatio-temporal correlation, motion prediction, Human3.6M
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